Limited-sampling strategies for anidulafungin in critically ill patients

Abstract

Efficacy of anidulafungin is driven by the area under the concentration-time curve (AUC)/MIC ratio. Determination of the anidulafungin AUC along with MIC values can therefore be useful. Since obtaining a full concentration-time curve to determine an AUC is not always feasible or appropriate, limited-sampling strategies may be useful in adequately estimating exposure. The objective of this study was to develop a model to predict the individual anidulafungin exposure in critically ill patients using limited-sampling strategies. Pharmacokinetic data were derived from 20 critically ill patients with invasive candidiasis treated with anidulafungin. These data were used to develop a two-compartment model in MW\Pharm using an iterative 2-stage Bayesian procedure. Limited-sampling strategies were subsequently investigated using two methods, a Bayesian analysis and a linear regression analysis. The best possible strategies for these two methods were evaluated by a Bland-Altman analysis for correlation of the predicted and observed AUC from 0 to 24 h (AUC0-24) values. Anidulafungin exposure can be adequately estimated with the concentration from a single sample drawn 12 h after the start of the infusion either by linear regression (R2=0.99; bias, 0.05%; root mean square error [RMSE], 3%) or using a population pharmacokinetic model (R2=0.89; bias, -0.1%; RMSE, 9%) in critically ill patients and also in less severely ill patients, as reflected by healthy volunteers. Limited sampling can be advantageous for future studies evaluating the pharmacokinetics and pharmacodynamics of anidulafungin and for therapeutic drug monitoring in selected patients. (This study has been registered at ClinicalTrials.gov under registration no. NCT01047267.).

Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Figures

FIG 1

Concentration-time curves of the 20 critically ill patients receiving 100 mg anidulafungin once daily.

FIG 2

The observed AUC0–24 versus the concentration measured 12 h after the start of the anidulafungin infusion.

FIG 3

Bland-Altman plots of the observed anidulafungin AUC0–24 versus the predicted AUC0–24 values with MW\Pharm (A) or regression analysis (B) for critically ill patients (n = 20) and Bland-Altman plots of the observed anidulafungin AUC0–24 versus the predicted AUC0–24 values with MW\Pharm (C) or regression analysis (D) for the healthy volunteers (n = 47).

FIG 4

The probability of target attainment versus the MIC for C. albicans (A) and C. glabrata (B) for critically ill patients. The bars represent the distribution of MICs based on EUCAST data.

FIG 5

Simulation of an increased anidulafungin dose of 170 mg in a patient with an AUC0–24 of 64.5 mg · h/liter after a loading dose of 200 mg on day 1 and a dose of 100 mg anidulafungin once daily on days 2 and 3.